There are different sources of noise in digital images acquired by image sensors in digital cameras, camcorders, and scanners, including fixed-pattern noise and temporal noise. Many factors determine the overall noise characteristics in an image: sensor type, pixel dimensions, temperature, exposure time, etc. Noise can also vary within an individual image. For digital cameras, darker regions may contain more noise than the brighter regions. Moreover, noise is space varying and channel dependent. The blue channel is usually the noisiest channel. Classical noise-reduction techniques remove noise from the Bayer image, before the color interpolation step. Thus, they often assume the noise to be uncorrelated for different pixels. The amount of noise which is not removed by the noise reduction technique is often spread in a neighborhood by the color-interpolation algorithm, which infers missing color components. Consequently, noise may have low-frequency (coarse-grain) and high-frequency (fine-grain) variations. High-frequency noise is relatively easier to remove than low-frequency noise, which may be difficult to distinguish from the real image signal. Moreover, noise is composed of two elements: fluctuations in color and luminance. Color or “chroma” noise is usually more unnatural in appearance than luminance noise, and can render images unusable if not kept under control. This kind of noise may appear as low-frequency, colored blobs in regions of low spatial frequency. These colored blobs may be irregularly shaped and are typically around 5 to 25, or more, pixels wide in a given direction, and usually are more pronounced in darker regions than in brighter regions.
A conventional approach to chroma-noise reduction is disclosed in U.S. Pat. No. 6,980,326, which is incorporated by reference. This approach includes using standard gray-scale image-noise-reduction techniques on each color channel separately, thus ignoring any interactions or correlations between the color channels. This technique can produce an excessive desaturation of genuine chrominance details. Early state-of-the-art techniques for removing chroma-noise artifacts usually convert the image into a luminance-chrominance space (YCrCb or CIELAB), apply some simple, but effective, methods such as mean, smoothing, or median filtering to the chrominance channels, and then convert the image back to the original color space. A shortcoming of this approach is that there is no discrimination between false colors and genuine chrominance details. Consequently, sharp, colored edges in the image begin to bleed color as the blurring becomes more aggressive. The problem of color bleeding can be contained using a small, fixed-blur kernel. But to remove low-frequency chroma blobs, large blur kernels are typically required.
U.S. Pat. No. 7,084,906, which is incorporated by reference, addresses the problem of removing color moiré artifacts from images by blurring chrominances in low-frequency activity neighborhoods, excluding edges. Down-sampled luminance and chrominance signals are used to separate the image into textured and non-textured regions. In particular, a binary texture map, which identifies low-frequency activity (LFA) areas, is constructed by threshold, erosion, and dilation operations. The average of the chrominances belonging to the LFA area within a 7×7 spider-shaped region is computed, thus removing color moiré artifacts. Also, to clean the non-textured regions of the image, a simple sigma filtering of the chroma channels is performed in a 3×3 support region (9×9 at the original pixel resolution). In particular, the absolute difference in chrominance between each neighboring pixel and the central pixel is computed. If both chroma channel values are within a threshold (usually set to 10 for 8-bit images), then the pixel is included in the cleaning calculations. This means that chroma noise is reduced by averaging pixels which have very similar chrominance values to the central one, thus avoiding color bleeding.
EP Patent 1093087 A2, which is incorporated by reference, provides a solution for reducing chroma noise with the use of large blur kernels without causing color bleeding at sharp, colored edges. More specifically, the disclosed technique firstly identify all the edges and boundaries in the image, and then allows a calculation-neighborhood region to adaptively grow until it encounters an edge. To create the edge map, four edge-detector filters are convolved with each channel (Lab), and the results are added together. Then the algorithm moves out in each of the eight compass directions, one pixel at a time, examining the edge-map values. If the difference between an edge-map value and the central-pixel value is less than a threshold, then that pixel is added to the smoothing neighborhood; otherwise, the growth of the smoothing region in that direction is stopped. A maximum radius value might be in the range of about 50 to 100 pixels. Within the smoothing region, the a and b channels are averaged. Eventually, the image is converted back to the original color space.
In the article “Multiresolution bilateral filtering for image denoising” by M. Zhang and B. K. Gunturk (IEEE Transactions on Image Processing, vol. 17, no. 12, December 2008), which is incorporated by reference, a multi-resolution technique is proposed to remove noise from images, because it is possible to distinguish between noise and image information better at one resolution level than another. Coarse-grain noise becomes fine-grain as the image is decomposed further into its subbands, and, hence, it could be eliminated at a lower level. The proposed framework decomposes the noisy signal into its frequency subbands with wavelet decomposition; as the signal is reconstructed back, bilateral filtering is applied to the approximation subbands. In addition, it is possible to apply wavelet thresholding to the detail subbands. As the number of decomposition levels increases, chroma noise is better removed, but bleeding effects increase as well.